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Contents
Economic Data and Trading Volume: An English Perspective
Unravelling Economic Surprises
Assuming a normal spread of surprises, about 68% align within one standard deviation of the mean when standardized using z-scores. For more details, kindly refer to the appendix regarding our use and calculation of z-scores.
Interest Rate Futures and Options Analysis
Following the 8:30:00am ET release, notable surprises in labour, inflation, and retail sales data showed significant impacts on trading volumes within one to ten minutes. For instance, non-farm payroll surprises had a minimal probability (0.003%) of being due to chance during the first five minutes.
Remarkably, in the very first minute post-release, around 20,663 interest rate futures contracts were traded, assuming no data surprises. A deviation in non-farm payroll data led to approximately 194,836 contracts being traded. Even smaller deviations in labour market figures, such as unemployment rates, significantly impacted trading volumes.
| Time Frame | Additional Futures Contracts Traded |
|---|---|
| 8:30 – 8:30:59 | 194,836 (due to NFP surprises) |
| 8:30 – 8:39:59 | Up to 730,000 (employment surprises) |
Economic Data Influences
Retail sales follow employment data in influence, with surprises typically generating an extra 80,000 contracts in the first minute. Meanwhile, inflation metrics like the CPI and PPI, although pivotal, have a more muted trading volume impact.
Trading Volumes and Market Days
Mondays see the least trading, averaging 286,000 to 921,000 fewer futures trades compared to other days. Conversely, Wednesdays claim the highest trading volumes. On average, FOMC announcement days contribute an extra 1,747,832 contracts to the daily interest rate options volume.
The 2021-2025 Context
The years post-2021 were volatile due to rising inflation, prompting significant market reactions. Core PCE inflation exceeded its target, influencing widespread rate hikes beginning March 2022, echoed globally. This period saw substantial interest rate uncertainties.
Related Information on Inflation Trends
Methodology Insights
Our analysis involves using a three-year rolling standard deviation for z-scores, thus reflecting the market’s evolving reactions. Time-based dummy variables allow for structural variance accounting in trading across different weekdays. FOMC days were also marked to highlight trading variations.
Appendix
Z-scores were calculated using a rolling standard deviation over three years to normalize surprises. For January 2021 data, for example, standard deviation spanned from January 2018 to December 2020. This rolling metric is critical to avoid future data biases in historical analyses.
In conclusion, understanding economic surprises’ impacts on trading volumes is essential. We hope this analysis provides clarity on how specific data segments influence trades, ensuring one is well-prepared for market movements.
